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AI for Drug Repurposing in the Pandemic Response
Artificial Intelligence in Covid-19 ; : 59-84, 2022.
Article in English | Scopus | ID: covidwho-20243965
ABSTRACT
Given the time criticality of finding treatments for the novel COVID-19 pandemic disease, drug repurposing has proved to be a vital strategy as the first response while de novo drug and vaccine developments are underway. Furthermore, Artificial Intelligence (AI) has also accelerated drug development in general. Key desirable features of AI that support a rapid and sustained response along the pandemic timeline include technical flexibility and efficiency (i.e. speed, resource-efficiency, algorithm adaptability), and clinical applicability and acceptability (i.e. scientific rigor, physiological applicability and practical implementation of proposed drugs). This chapter reviews a selection of AI-based applications used in drug development targeting COVID-19, including IDentif.AI-a small data platform for a rapid identification of optimal drug combinations, to illustrate the potential of AI in drug repurposing. The benefits and limitations of using Real-World Data are also discussed. The response to the COVID-19 pandemic has offered multiple learnings which highlight the need to strengthen both short- and long-term strategies in developing AI technologies, scientific and regulatory frameworks as well as worldwide collaborations to enable effective preparedness for future epidemic and pandemic risks. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Topics: Vaccines Language: English Journal: Artificial Intelligence in Covid-19 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Topics: Vaccines Language: English Journal: Artificial Intelligence in Covid-19 Year: 2022 Document Type: Article